Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	104,004
2	South Dakota	90,954
3	Iowa	73,099
4	Wisconsin	70,714
5	Nebraska	66,380
6	Utah	61,056
7	Montana	58,249
8	Wyoming	57,545
9	Illinois	57,436
10	Idaho	57,165
11	Minnesota	56,532
12	Kansas	55,067
13	Tennessee	53,653
14	Rhode Island	53,544
15	Arkansas	52,143
16	Mississippi	51,499
17	Missouri	51,321
18	Alabama	50,890
19	Indiana	50,755
20	Louisiana	49,994
21	Oklahoma	49,973
22	Nevada	49,583
23	Florida	46,527
24	New Mexico	46,305
25	Arizona	44,931
26	Alaska	44,299
27	Texas	43,528
28	Georgia	42,790
29	South Carolina	42,241
30	Kentucky	41,017
31	Colorado	40,721
32	Michigan	38,887
33	New Jersey	38,181
34	Delaware	36,614
35	Ohio	36,021
36	North Carolina	34,798
37	New York	33,554
38	Connecticut	32,899
39	Maryland	32,868
40	Massachusetts	32,808
41	California	31,311
42	District of Columbia	30,537
43	Pennsylvania	28,665
44	Virginia	27,864
45	Puerto Rico	26,978
46	West Virginia	26,695
47	Washington	22,610
48	Oregon	17,889
49	New Hampshire	15,440
50	Hawaii	12,717
51	Maine	8,746
52	Vermont	6,686

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Minnesota	1,404
2	North Dakota	883
3	Wyoming	882
4	Rhode Island	871
5	Oklahoma	857
6	New Mexico	833
7	Tennessee	825
8	South Dakota	822
9	Alaska	817
10	Montana	767
11	Nebraska	748
12	Indiana	715
13	Wisconsin	696
14	Colorado	692
15	Michigan	645
16	Kentucky	639
17	Ohio	606
18	Nevada	603
19	Kansas	593
20	Utah	592
21	Iowa	588
22	Missouri	583
23	Illinois	570
24	Idaho	561
25	Mississippi	546
26	West Virginia	520
27	Delaware	507
28	Pennsylvania	458
29	Alabama	452
30	Connecticut	440
31	New Hampshire	412
32	Arkansas	406
33	New Jersey	406
34	Texas	385
35	Arizona	382
36	California	372
37	Oregon	365
38	New York	335
39	Massachusetts	332
40	Florida	315
41	Washington	304
42	Maryland	303
43	North Carolina	295
44	District of Columbia	290
45	Virginia	288
46	South Carolina	282
47	Georgia	200
48	Puerto Rico	194
49	Louisiana	129
50	Maine	122
51	Vermont	89
52	Hawaii	49

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	1,913
2	New York	1,755
3	Massachusetts	1,559
4	Connecticut	1,408
5	Louisiana	1,381
6	Rhode Island	1,296
7	Mississippi	1,279
8	North Dakota	1,224
9	South Dakota	1,069
10	Illinois	1,027
11	District of Columbia	963
12	Michigan	957
13	Arizona	912
14	Georgia	866
15	Florida	865
16	Indiana	850
17	South Carolina	850
18	Arkansas	829
19	Pennsylvania	815
20	Delaware	792
21	Iowa	768
22	Maryland	767
23	Texas	757
24	New Mexico	747
25	Alabama	729
26	Nevada	697
27	Tennessee	665
28	Missouri	648
29	Minnesota	647
30	Montana	639
31	Wisconsin	601
32	Ohio	549
33	Nebraska	537
34	Kansas	535
35	Colorado	531
36	Idaho	524
37	North Carolina	504
38	California	486
39	Virginia	475
40	Kentucky	462
41	Oklahoma	440
42	West Virginia	410
43	New Hampshire	386
44	Washington	380
45	Wyoming	371
46	Puerto Rico	346
47	Utah	271
48	Oregon	217
49	Hawaii	170
50	Alaska	157
51	Maine	144
52	Vermont	110

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	South Dakota	21
2	New Mexico	10
3	North Dakota	10
4	Illinois	8
5	Montana	8
6	Rhode Island	8
7	Arkansas	7
8	Michigan	7
9	Indiana	6
10	Iowa	6
11	Minnesota	6
12	Connecticut	5
13	Massachusetts	5
14	Idaho	4
15	Kentucky	4
16	Mississippi	4
17	Nebraska	4
18	Nevada	4
19	West Virginia	4
20	Colorado	3
21	Delaware	3
22	Florida	3
23	Kansas	3
24	Maryland	3
25	Oklahoma	3
26	Pennsylvania	3
27	Puerto Rico	3
28	Tennessee	3
29	Wisconsin	3
30	Arizona	2
31	Georgia	2
32	Louisiana	2
33	Missouri	2
34	New Hampshire	2
35	New York	2
36	Ohio	2
37	Oregon	2
38	South Carolina	2
39	Texas	2
40	Utah	2
41	Washington	2
42	District of Columbia	1
43	New Jersey	1
44	North Carolina	1
45	Vermont	1
46	Alabama	0
47	Alaska	0
48	California	0
49	Hawaii	0
50	Maine	0
51	Virginia	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Crowley	Colorado	204,422	1	99
Norton	Kansas	203,693	2	99
Bon Homme	South Dakota	197,508	3	99
Buffalo	South Dakota	192,661	4	99
Lincoln	Arkansas	189,189	5	99
Davidson	Tennessee	66,714	519	83
Richland	South Carolina	49,178	1207	61
York	South Carolina	34,846	2041	35
Orange	California	25,456	2519	19
Pierce	Washington	20,170	2723	13

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Gove	Kansas	7,587	1	99
Jerauld	South Dakota	6,458	2	99
Dickey	North Dakota	5,747	3	99
Hancock	Georgia	5,439	4	99
Emporia city	Virginia	5,238	5	99
Richland	South Carolina	726	1266	59
Davidson	Tennessee	606	1521	51
Orange	California	497	1789	43
York	South Carolina	484	1831	41
Pierce	Washington	351	2201	29

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.